Overview

Dataset statistics

Number of variables33
Number of observations867372
Missing cells927966
Missing cells (%)3.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory218.4 MiB
Average record size in memory264.0 B

Variable types

Categorical16
Numeric15
Unsupported1
Boolean1

Alerts

Accident_Index has a high cardinality: 558022 distinct values High cardinality
Date has a high cardinality: 2190 distinct values High cardinality
Time has a high cardinality: 1439 distinct values High cardinality
Local_Authority_(Highway) has a high cardinality: 207 distinct values High cardinality
LSOA_of_Accident_Location has a high cardinality: 34140 distinct values High cardinality
Location_Easting_OSGR is highly correlated with LongitudeHigh correlation
Location_Northing_OSGR is highly correlated with LatitudeHigh correlation
Longitude is highly correlated with Location_Easting_OSGRHigh correlation
Latitude is highly correlated with Location_Northing_OSGRHigh correlation
Police_Force is highly correlated with Local_Authority_(District)High correlation
Local_Authority_(District) is highly correlated with Police_ForceHigh correlation
Speed_limit is highly correlated with Urban_or_Rural_AreaHigh correlation
Urban_or_Rural_Area is highly correlated with Speed_limitHigh correlation
Location_Easting_OSGR is highly correlated with LongitudeHigh correlation
Location_Northing_OSGR is highly correlated with LatitudeHigh correlation
Longitude is highly correlated with Location_Easting_OSGRHigh correlation
Latitude is highly correlated with Location_Northing_OSGRHigh correlation
Police_Force is highly correlated with Local_Authority_(District)High correlation
Local_Authority_(District) is highly correlated with Police_ForceHigh correlation
Speed_limit is highly correlated with Urban_or_Rural_AreaHigh correlation
Urban_or_Rural_Area is highly correlated with Speed_limitHigh correlation
Location_Easting_OSGR is highly correlated with LongitudeHigh correlation
Location_Northing_OSGR is highly correlated with LatitudeHigh correlation
Longitude is highly correlated with Location_Easting_OSGRHigh correlation
Latitude is highly correlated with Location_Northing_OSGRHigh correlation
Police_Force is highly correlated with Local_Authority_(District)High correlation
Local_Authority_(District) is highly correlated with Police_ForceHigh correlation
Speed_limit is highly correlated with Urban_or_Rural_AreaHigh correlation
Urban_or_Rural_Area is highly correlated with Speed_limitHigh correlation
Location_Easting_OSGR is highly correlated with Location_Northing_OSGR and 4 other fieldsHigh correlation
Location_Northing_OSGR is highly correlated with Location_Easting_OSGR and 4 other fieldsHigh correlation
Longitude is highly correlated with Location_Easting_OSGR and 4 other fieldsHigh correlation
Latitude is highly correlated with Location_Easting_OSGR and 4 other fieldsHigh correlation
Police_Force is highly correlated with Location_Easting_OSGR and 4 other fieldsHigh correlation
Local_Authority_(District) is highly correlated with Location_Easting_OSGR and 4 other fieldsHigh correlation
1st_Road_Class is highly correlated with Road_Type and 1 other fieldsHigh correlation
1st_Road_Number is highly correlated with 2nd_Road_NumberHigh correlation
Road_Type is highly correlated with 1st_Road_ClassHigh correlation
Speed_limit is highly correlated with 1st_Road_Class and 1 other fieldsHigh correlation
Junction_Control is highly correlated with 2nd_Road_ClassHigh correlation
2nd_Road_Class is highly correlated with Junction_ControlHigh correlation
2nd_Road_Number is highly correlated with 1st_Road_NumberHigh correlation
Weather_Conditions is highly correlated with Road_Surface_ConditionsHigh correlation
Road_Surface_Conditions is highly correlated with Weather_ConditionsHigh correlation
Urban_or_Rural_Area is highly correlated with Speed_limitHigh correlation
Junction_Detail has 867372 (100.0%) missing values Missing
LSOA_of_Accident_Location has 58466 (6.7%) missing values Missing
Junction_Detail is an unsupported type, check if it needs cleaning or further analysis Unsupported
1st_Road_Number has 236822 (27.3%) zeros Zeros
2nd_Road_Number has 669327 (77.2%) zeros Zeros

Reproduction

Analysis started2022-01-09 14:19:04.613977
Analysis finished2022-01-09 14:28:41.340599
Duration9 minutes and 36.73 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Accident_Index
Categorical

HIGH CARDINALITY

Distinct558022
Distinct (%)64.3%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
2.01E+12
137424 
2.00913E+12
 
6254
2.01013E+12
 
5760
2.01113E+12
 
5402
2.00946E+12
 
5158
Other values (558017)
707374 

Length

Max length255
Median length13
Mean length11.82357282
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique557817 ?
Unique (%)64.3%

Sample

1st row200901BS70001
2nd row200901BS70002
3rd row200901BS70003
4th row200901BS70004
5th row200901BS70005

Common Values

ValueCountFrequency (%)
2.01E+12137424
 
15.8%
2.00913E+126254
 
0.7%
2.01013E+125760
 
0.7%
2.01113E+125402
 
0.6%
2.00946E+125158
 
0.6%
2.01144E+124999
 
0.6%
2.01146E+124867
 
0.6%
2.01046E+124837
 
0.6%
2.01044E+124809
 
0.6%
2.00944E+124683
 
0.5%
Other values (558012)683179
78.8%

Length

2022-01-09T17:28:41.819367image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2.01e+12137424
 
15.8%
2.00913e+126254
 
0.7%
2.01013e+125760
 
0.7%
2.01113e+125402
 
0.6%
2.00946e+125158
 
0.6%
2.01144e+124999
 
0.6%
2.01146e+124867
 
0.6%
2.01046e+124837
 
0.6%
2.01044e+124809
 
0.6%
2.00944e+124683
 
0.5%
Other values (558012)683179
78.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Location_Easting_OSGR
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct60957
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean439350.7448
Minimum64950
Maximum655370
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 MiB
2022-01-09T17:28:42.094880image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum64950
5-th percentile263690
Q1375230
median439790
Q3523520
95-th percentile577090
Maximum655370
Range590420
Interquartile range (IQR)148290

Descriptive statistics

Standard deviation95451.99925
Coefficient of variation (CV)0.2172569419
Kurtosis-0.4163682343
Mean439350.7448
Median Absolute Deviation (MAD)77230
Skewness-0.3257069323
Sum3.810805342 × 1011
Variance9111084161
MonotonicityNot monotonic
2022-01-09T17:28:42.276062image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
533650222
 
< 0.1%
531170201
 
< 0.1%
531180177
 
< 0.1%
530980169
 
< 0.1%
533470159
 
< 0.1%
532440157
 
< 0.1%
529210156
 
< 0.1%
530020156
 
< 0.1%
534950150
 
< 0.1%
531000149
 
< 0.1%
Other values (60947)865676
99.8%
ValueCountFrequency (%)
649501
< 0.1%
653501
< 0.1%
656701
< 0.1%
656901
< 0.1%
658601
< 0.1%
659501
< 0.1%
667101
< 0.1%
682901
< 0.1%
688801
< 0.1%
737601
< 0.1%
ValueCountFrequency (%)
6553702
 
< 0.1%
6552901
 
< 0.1%
6552802
 
< 0.1%
6552302
 
< 0.1%
6551801
 
< 0.1%
6551601
 
< 0.1%
6551502
 
< 0.1%
6551401
 
< 0.1%
6551204
< 0.1%
6551105
< 0.1%

Location_Northing_OSGR
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct84077
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean299324.9649
Minimum10520
Maximum1205100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 MiB
2022-01-09T17:28:42.760350image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum10520
5-th percentile104210
Q1178530
median266320
Q3396910
95-th percentile662790
Maximum1205100
Range1194580
Interquartile range (IQR)218380

Descriptive statistics

Standard deviation161294.4849
Coefficient of variation (CV)0.5388607828
Kurtosis0.8525991951
Mean299324.9649
Median Absolute Deviation (MAD)98110
Skewness1.027835807
Sum2.596260935 × 1011
Variance2.601591086 × 1010
MonotonicityNot monotonic
2022-01-09T17:28:43.029349image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
181310237
 
< 0.1%
181170205
 
< 0.1%
181110192
 
< 0.1%
179660188
 
< 0.1%
181060184
 
< 0.1%
181050179
 
< 0.1%
178620179
 
< 0.1%
181120175
 
< 0.1%
180820170
 
< 0.1%
181370170
 
< 0.1%
Other values (84067)865493
99.8%
ValueCountFrequency (%)
105201
< 0.1%
105302
< 0.1%
105601
< 0.1%
106501
< 0.1%
106701
< 0.1%
108401
< 0.1%
108601
< 0.1%
111001
< 0.1%
116201
< 0.1%
124601
< 0.1%
ValueCountFrequency (%)
12051001
< 0.1%
12039001
< 0.1%
11980001
< 0.1%
11917001
< 0.1%
11915001
< 0.1%
11910301
< 0.1%
11896001
< 0.1%
11861001
< 0.1%
11838301
< 0.1%
11786001
< 0.1%

Longitude
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct665683
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.440749832
Minimum-7.516225
Maximum1.759398
Zeros0
Zeros (%)0.0%
Negative755310
Negative (%)87.1%
Memory size6.6 MiB
2022-01-09T17:28:43.755658image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-7.516225
5-th percentile-4.06497235
Q1-2.3711
median-1.405808
Q3-0.21473
95-th percentile0.5489041
Maximum1.759398
Range9.275623
Interquartile range (IQR)2.15637

Descriptive statistics

Standard deviation1.40324017
Coefficient of variation (CV)-0.9739651807
Kurtosis-0.3704684734
Mean-1.440749832
Median Absolute Deviation (MAD)1.1174075
Skewness-0.3658904361
Sum-1249666.063
Variance1.969082976
MonotonicityNot monotonic
2022-01-09T17:28:43.936652image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3.31059643
 
< 0.1%
-0.97761142
 
< 0.1%
-3.24169438
 
< 0.1%
-1.23439333
 
< 0.1%
-1.87104331
 
< 0.1%
-0.17344530
 
< 0.1%
-1.21669430
 
< 0.1%
-0.11506329
 
< 0.1%
-0.07720929
 
< 0.1%
-1.19228629
 
< 0.1%
Other values (665673)867038
> 99.9%
ValueCountFrequency (%)
-7.5162251
< 0.1%
-7.5074681
< 0.1%
-7.5072071
< 0.1%
-7.5041211
< 0.1%
-7.4989731
< 0.1%
-7.4974611
< 0.1%
-7.491831
< 0.1%
-7.4656091
< 0.1%
-7.4602591
< 0.1%
-7.4500151
< 0.1%
ValueCountFrequency (%)
1.7593981
< 0.1%
1.7593821
< 0.1%
1.7583371
< 0.1%
1.758191
< 0.1%
1.7581061
< 0.1%
1.7579151
< 0.1%
1.7579071
< 0.1%
1.756511
< 0.1%
1.756431
< 0.1%
1.7563471
< 0.1%

Latitude
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct638360
Distinct (%)73.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.58186561
Minimum49.914488
Maximum60.724682
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 MiB
2022-01-09T17:28:44.172606image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum49.914488
5-th percentile50.82470565
Q151.492738
median52.2882425
Q353.4672835
95-th percentile55.838292
Maximum60.724682
Range10.810194
Interquartile range (IQR)1.9745455

Descriptive statistics

Standard deviation1.452368685
Coefficient of variation (CV)0.02762109462
Kurtosis0.8233613321
Mean52.58186561
Median Absolute Deviation (MAD)0.889619
Skewness1.01879047
Sum45608037.94
Variance2.109374798
MonotonicityNot monotonic
2022-01-09T17:28:44.312608image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51.50669344
 
< 0.1%
52.94971942
 
< 0.1%
52.45879840
 
< 0.1%
51.52695638
 
< 0.1%
52.98985733
 
< 0.1%
51.56010832
 
< 0.1%
51.48207632
 
< 0.1%
52.95505830
 
< 0.1%
53.79242330
 
< 0.1%
52.9388630
 
< 0.1%
Other values (638350)867021
> 99.9%
ValueCountFrequency (%)
49.9144881
< 0.1%
49.9145131
< 0.1%
49.9147011
< 0.1%
49.9148041
< 0.1%
49.915731
< 0.1%
49.9159871
< 0.1%
49.9177031
< 0.1%
49.9179131
< 0.1%
49.9208951
< 0.1%
49.9252251
< 0.1%
ValueCountFrequency (%)
60.7246821
< 0.1%
60.7147741
< 0.1%
60.6620431
< 0.1%
60.6057431
< 0.1%
60.6041171
< 0.1%
60.5980551
< 0.1%
60.5865861
< 0.1%
60.5552341
< 0.1%
60.5356231
< 0.1%
60.489821
< 0.1%

Police_Force
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.53229526
Minimum1
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 MiB
2022-01-09T17:28:44.447561image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q17
median30
Q346
95-th percentile94
Maximum98
Range97
Interquartile range (IQR)39

Descriptive statistics

Standard deviation25.63090547
Coefficient of variation (CV)0.839468676
Kurtosis0.3095302262
Mean30.53229526
Median Absolute Deviation (MAD)18
Skewness0.8503063383
Sum26482858
Variance656.9433153
MonotonicityNot monotonic
2022-01-09T17:28:44.645630image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1131752
 
15.2%
2036445
 
4.2%
634731
 
4.0%
1331856
 
3.7%
4331679
 
3.7%
4425849
 
3.0%
4625747
 
3.0%
5024887
 
2.9%
9724438
 
2.8%
424003
 
2.8%
Other values (41)475985
54.9%
ValueCountFrequency (%)
1131752
15.2%
37617
 
0.9%
424003
 
2.8%
518625
 
2.1%
634731
 
4.0%
716831
 
1.9%
1020032
 
2.3%
117922
 
0.9%
1211789
 
1.4%
1331856
 
3.7%
ValueCountFrequency (%)
982000
 
0.2%
9724438
2.8%
962957
 
0.3%
9512288
1.4%
942967
 
0.3%
934377
 
0.5%
925686
 
0.7%
913382
 
0.4%
637955
 
0.9%
6215965
1.8%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
3
741142 
2
114752 
1
 
11478

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row3
4th row2
5th row2

Common Values

ValueCountFrequency (%)
3741142
85.4%
2114752
 
13.2%
111478
 
1.3%

Length

2022-01-09T17:28:45.043774image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-09T17:28:45.146746image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
3741142
85.4%
2114752
 
13.2%
111478
 
1.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Number_of_Vehicles
Real number (ℝ≥0)

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.831141655
Minimum1
Maximum34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 MiB
2022-01-09T17:28:45.243784image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile3
Maximum34
Range33
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.715248083
Coefficient of variation (CV)0.3906022677
Kurtosis22.46545236
Mean1.831141655
Median Absolute Deviation (MAD)0
Skewness1.883984657
Sum1588281
Variance0.5115798203
MonotonicityNot monotonic
2022-01-09T17:28:45.376747image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2514004
59.3%
1263812
30.4%
369962
 
8.1%
414611
 
1.7%
53280
 
0.4%
61019
 
0.1%
7346
 
< 0.1%
8165
 
< 0.1%
987
 
< 0.1%
1031
 
< 0.1%
Other values (14)55
 
< 0.1%
ValueCountFrequency (%)
1263812
30.4%
2514004
59.3%
369962
 
8.1%
414611
 
1.7%
53280
 
0.4%
61019
 
0.1%
7346
 
< 0.1%
8165
 
< 0.1%
987
 
< 0.1%
1031
 
< 0.1%
ValueCountFrequency (%)
341
< 0.1%
321
< 0.1%
291
< 0.1%
221
< 0.1%
202
< 0.1%
191
< 0.1%
182
< 0.1%
171
< 0.1%
162
< 0.1%
152
< 0.1%

Number_of_Casualties
Real number (ℝ≥0)

Distinct39
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.355924563
Minimum1
Maximum87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 MiB
2022-01-09T17:28:45.529786image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum87
Range86
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8227847945
Coefficient of variation (CV)0.6068072052
Kurtosis304.897789
Mean1.355924563
Median Absolute Deviation (MAD)0
Skewness7.440040159
Sum1176091
Variance0.676974818
MonotonicityNot monotonic
2022-01-09T17:28:45.704752image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
1662403
76.4%
2141250
 
16.3%
340167
 
4.6%
414754
 
1.7%
55350
 
0.6%
62032
 
0.2%
7700
 
0.1%
8301
 
< 0.1%
9132
 
< 0.1%
1083
 
< 0.1%
Other values (29)200
 
< 0.1%
ValueCountFrequency (%)
1662403
76.4%
2141250
 
16.3%
340167
 
4.6%
414754
 
1.7%
55350
 
0.6%
62032
 
0.2%
7700
 
0.1%
8301
 
< 0.1%
9132
 
< 0.1%
1083
 
< 0.1%
ValueCountFrequency (%)
871
< 0.1%
631
< 0.1%
512
< 0.1%
481
< 0.1%
451
< 0.1%
432
< 0.1%
421
< 0.1%
411
< 0.1%
401
< 0.1%
362
< 0.1%

Date
Categorical

HIGH CARDINALITY

Distinct2190
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
21/10/2005
 
822
18/11/2005
 
787
29/09/2006
 
784
22/09/2006
 
780
7/12/2005
 
774
Other values (2185)
863425 

Length

Max length10
Median length10
Mean length9.413204484
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1/1/2009
2nd row5/1/2009
3rd row4/1/2009
4th row5/1/2009
5th row6/1/2009

Common Values

ValueCountFrequency (%)
21/10/2005822
 
0.1%
18/11/2005787
 
0.1%
29/09/2006784
 
0.1%
22/09/2006780
 
0.1%
7/12/2005774
 
0.1%
1/12/2006750
 
0.1%
30/09/2005749
 
0.1%
9/12/2005748
 
0.1%
12/10/2005746
 
0.1%
19/12/2005744
 
0.1%
Other values (2180)859688
99.1%

Length

2022-01-09T17:28:46.072376image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
21/10/2005822
 
0.1%
18/11/2005787
 
0.1%
29/09/2006784
 
0.1%
22/09/2006780
 
0.1%
7/12/2005774
 
0.1%
1/12/2006750
 
0.1%
30/09/2005749
 
0.1%
9/12/2005748
 
0.1%
12/10/2005746
 
0.1%
19/12/2005744
 
0.1%
Other values (2180)859688
99.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Day_of_Week
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.12645324
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 MiB
2022-01-09T17:28:46.182411image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.926812267
Coefficient of variation (CV)0.4669415003
Kurtosis-1.188999884
Mean4.12645324
Median Absolute Deviation (MAD)2
Skewness-0.07423389539
Sum3579170
Variance3.712605512
MonotonicityNot monotonic
2022-01-09T17:28:46.293372image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6143377
16.5%
4130778
15.1%
5130008
15.0%
3128397
14.8%
2122229
14.1%
7117254
13.5%
195329
11.0%
ValueCountFrequency (%)
195329
11.0%
2122229
14.1%
3128397
14.8%
4130778
15.1%
5130008
15.0%
6143377
16.5%
7117254
13.5%
ValueCountFrequency (%)
7117254
13.5%
6143377
16.5%
5130008
15.0%
4130778
15.1%
3128397
14.8%
2122229
14.1%
195329
11.0%

Time
Categorical

HIGH CARDINALITY

Distinct1439
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
17:00
 
8529
17:30
 
8031
16:00
 
7720
15:30
 
7684
18:00
 
7620
Other values (1434)
827788 

Length

Max length5
Median length5
Mean length4.764020513
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row15:11
2nd row10:59
3rd row14:19
4th row8:10
5th row17:25

Common Values

ValueCountFrequency (%)
17:008529
 
1.0%
17:308031
 
0.9%
16:007720
 
0.9%
15:307684
 
0.9%
18:007620
 
0.9%
16:307411
 
0.9%
15:006775
 
0.8%
8:306772
 
0.8%
14:006218
 
0.7%
13:006205
 
0.7%
Other values (1429)794407
91.6%

Length

2022-01-09T17:28:46.529158image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
17:008529
 
1.0%
17:308031
 
0.9%
16:007720
 
0.9%
15:307684
 
0.9%
18:007620
 
0.9%
16:307411
 
0.9%
15:006775
 
0.8%
8:306772
 
0.8%
14:006218
 
0.7%
13:006205
 
0.7%
Other values (1429)794407
91.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Local_Authority_(District)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct416
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean350.8307036
Minimum1
Maximum941
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 MiB
2022-01-09T17:28:46.682198image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q1112
median323
Q3531
95-th percentile919
Maximum941
Range940
Interquartile range (IQR)419

Descriptive statistics

Standard deviation260.5504424
Coefficient of variation (CV)0.7426671605
Kurtosis-0.5250859616
Mean350.8307036
Median Absolute Deviation (MAD)209
Skewness0.512357379
Sum304300729
Variance67886.53305
MonotonicityNot monotonic
2022-01-09T17:28:46.860156image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30016566
 
1.9%
20411304
 
1.3%
1028873
 
1.0%
18861
 
1.0%
9267944
 
0.9%
2007829
 
0.9%
917564
 
0.9%
2157444
 
0.9%
96522
 
0.8%
9236399
 
0.7%
Other values (406)778066
89.7%
ValueCountFrequency (%)
18861
1.0%
24940
0.6%
34127
0.5%
44806
0.6%
54954
0.6%
63701
0.4%
74298
0.5%
85328
0.6%
96522
0.8%
104323
0.5%
ValueCountFrequency (%)
941190
 
< 0.1%
9402130
0.2%
9391213
 
0.1%
9383079
0.4%
9371230
 
0.1%
936195
 
< 0.1%
9351899
0.2%
9341820
0.2%
933147
 
< 0.1%
9323350
0.4%

Local_Authority_(Highway)
Categorical

HIGH CARDINALITY

Distinct207
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
E10000016
 
22578
E10000030
 
21430
E10000017
 
19265
E10000012
 
18446
E10000014
 
17105
Other values (202)
768548 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE09000020
2nd rowE09000020
3rd rowE09000020
4th rowE09000020
5th rowE09000020

Common Values

ValueCountFrequency (%)
E1000001622578
 
2.6%
E1000003021430
 
2.5%
E1000001719265
 
2.2%
E1000001218446
 
2.1%
E1000001417105
 
2.0%
E0800002516566
 
1.9%
E1000001515744
 
1.8%
E1000002813659
 
1.6%
E1000001912098
 
1.4%
E1000002411666
 
1.3%
Other values (197)698815
80.6%

Length

2022-01-09T17:28:47.043156image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
e1000001622578
 
2.6%
e1000003021430
 
2.5%
e1000001719265
 
2.2%
e1000001218446
 
2.1%
e1000001417105
 
2.0%
e0800002516566
 
1.9%
e1000001515744
 
1.8%
e1000002813659
 
1.6%
e1000001912098
 
1.4%
e1000002411666
 
1.3%
Other values (197)698815
80.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

1st_Road_Class
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.091354113
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 MiB
2022-01-09T17:28:47.176155image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q13
median4
Q36
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.431841987
Coefficient of variation (CV)0.3499677484
Kurtosis-1.063033778
Mean4.091354113
Median Absolute Deviation (MAD)1
Skewness0.1056652583
Sum3548726
Variance2.050171475
MonotonicityNot monotonic
2022-01-09T17:28:47.291193image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3393384
45.4%
6250346
28.9%
4109888
 
12.7%
577742
 
9.0%
133788
 
3.9%
22224
 
0.3%
ValueCountFrequency (%)
133788
 
3.9%
22224
 
0.3%
3393384
45.4%
4109888
 
12.7%
577742
 
9.0%
6250346
28.9%
ValueCountFrequency (%)
6250346
28.9%
577742
 
9.0%
4109888
 
12.7%
3393384
45.4%
22224
 
0.3%
133788
 
3.9%

1st_Road_Number
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct6329
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1002.7558
Minimum-1
Maximum9999
Zeros236822
Zeros (%)27.3%
Negative2
Negative (%)< 0.1%
Memory size6.6 MiB
2022-01-09T17:28:47.464879image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median124
Q3709
95-th percentile5305
Maximum9999
Range10000
Interquartile range (IQR)709

Descriptive statistics

Standard deviation1822.901827
Coefficient of variation (CV)1.81789208
Kurtosis3.399846986
Mean1002.7558
Median Absolute Deviation (MAD)124
Skewness2.085345911
Sum869762304
Variance3322971.072
MonotonicityNot monotonic
2022-01-09T17:28:47.720883image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0236822
27.3%
111246
 
1.3%
69532
 
1.1%
48273
 
1.0%
255892
 
0.7%
55743
 
0.7%
405632
 
0.6%
385198
 
0.6%
34957
 
0.6%
234372
 
0.5%
Other values (6319)569705
65.7%
ValueCountFrequency (%)
-12
 
< 0.1%
0236822
27.3%
111246
 
1.3%
23796
 
0.4%
34957
 
0.6%
48273
 
1.0%
55743
 
0.7%
69532
 
1.1%
71047
 
0.1%
82481
 
0.3%
ValueCountFrequency (%)
9999172
< 0.1%
99631
 
< 0.1%
99621
 
< 0.1%
99501
 
< 0.1%
99101
 
< 0.1%
98752
 
< 0.1%
98551
 
< 0.1%
98541
 
< 0.1%
98531
 
< 0.1%
98402
 
< 0.1%

Road_Type
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
Single carriageway
648574 
Dual carriageway
128842 
Roundabout
 
57237
One way street
 
18525
Slip road
 
8932

Length

Max length18
Median length18
Mean length16.93015915
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOne way street
2nd rowSingle carriageway
3rd rowSingle carriageway
4th rowSingle carriageway
5th rowSingle carriageway

Common Values

ValueCountFrequency (%)
Single carriageway648574
74.8%
Dual carriageway128842
 
14.9%
Roundabout57237
 
6.6%
One way street18525
 
2.1%
Slip road8932
 
1.0%
Unknown5262
 
0.6%

Length

2022-01-09T17:28:47.986880image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-09T17:28:48.124879image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
carriageway777416
46.0%
single648574
38.4%
dual128842
 
7.6%
roundabout57237
 
3.4%
one18525
 
1.1%
way18525
 
1.1%
street18525
 
1.1%
slip8932
 
0.5%
road8932
 
0.5%
unknown5262
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Speed_limit
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.16844791
Minimum10
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 MiB
2022-01-09T17:28:48.234879image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile30
Q130
median30
Q350
95-th percentile70
Maximum70
Range60
Interquartile range (IQR)20

Descriptive statistics

Standard deviation14.19016509
Coefficient of variation (CV)0.3622856111
Kurtosis-0.4569288547
Mean39.16844791
Median Absolute Deviation (MAD)0
Skewness1.089020897
Sum33973615
Variance201.3607852
MonotonicityNot monotonic
2022-01-09T17:28:48.342917image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
30558783
64.4%
60141667
 
16.3%
4069881
 
8.1%
7063628
 
7.3%
5026426
 
3.0%
206970
 
0.8%
1010
 
< 0.1%
157
 
< 0.1%
ValueCountFrequency (%)
1010
 
< 0.1%
157
 
< 0.1%
206970
 
0.8%
30558783
64.4%
4069881
 
8.1%
5026426
 
3.0%
60141667
 
16.3%
7063628
 
7.3%
ValueCountFrequency (%)
7063628
 
7.3%
60141667
 
16.3%
5026426
 
3.0%
4069881
 
8.1%
30558783
64.4%
206970
 
0.8%
157
 
< 0.1%
1010
 
< 0.1%

Junction_Detail
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing867372
Missing (%)100.0%
Memory size6.6 MiB

Junction_Control
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
Giveway or uncontrolled
419618 
No_Junction_Control
350755 
Automatic traffic signal
89941 
Stop Sign
 
5571
Authorised person
 
1487

Length

Max length24
Median length23
Mean length21.38593475
Min length9

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGiveway or uncontrolled
2nd rowGiveway or uncontrolled
3rd rowGiveway or uncontrolled
4th rowAutomatic traffic signal
5th rowAutomatic traffic signal

Common Values

ValueCountFrequency (%)
Giveway or uncontrolled419618
48.4%
No_Junction_Control350755
40.4%
Automatic traffic signal89941
 
10.4%
Stop Sign5571
 
0.6%
Authorised person1487
 
0.2%

Length

2022-01-09T17:28:48.469879image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-09T17:28:48.607878image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
giveway419618
22.2%
or419618
22.2%
uncontrolled419618
22.2%
no_junction_control350755
18.5%
automatic89941
 
4.7%
traffic89941
 
4.7%
signal89941
 
4.7%
stop5571
 
0.3%
sign5571
 
0.3%
authorised1487
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

2nd_Road_Class
Real number (ℝ)

HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.642384121
Minimum-1
Maximum6
Zeros0
Zeros (%)0.0%
Negative358630
Negative (%)41.3%
Memory size6.6 MiB
2022-01-09T17:28:48.818223image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median3
Q36
95-th percentile6
Maximum6
Range7
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.205619375
Coefficient of variation (CV)1.213154193
Kurtosis-1.822725238
Mean2.642384121
Median Absolute Deviation (MAD)3
Skewness-0.1344922834
Sum2291930
Variance10.27599558
MonotonicityNot monotonic
2022-01-09T17:28:48.967225image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
-1358630
41.3%
6340640
39.3%
387168
 
10.0%
540530
 
4.7%
433823
 
3.9%
15888
 
0.7%
2693
 
0.1%
ValueCountFrequency (%)
-1358630
41.3%
15888
 
0.7%
2693
 
0.1%
387168
 
10.0%
433823
 
3.9%
540530
 
4.7%
6340640
39.3%
ValueCountFrequency (%)
6340640
39.3%
540530
 
4.7%
433823
 
3.9%
387168
 
10.0%
2693
 
0.1%
15888
 
0.7%
-1358630
41.3%

2nd_Road_Number
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct6670
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean381.583979
Minimum-1
Maximum9999
Zeros669327
Zeros (%)77.2%
Negative9146
Negative (%)1.1%
Memory size6.6 MiB
2022-01-09T17:28:49.216565image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median0
Q30
95-th percentile3408
Maximum9999
Range10000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1305.236884
Coefficient of variation (CV)3.420575694
Kurtosis16.99951133
Mean381.583979
Median Absolute Deviation (MAD)0
Skewness4.094705262
Sum330975259
Variance1703643.323
MonotonicityNot monotonic
2022-01-09T17:28:49.398565image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0669327
77.2%
-19146
 
1.1%
12119
 
0.2%
41713
 
0.2%
61675
 
0.2%
401219
 
0.1%
51084
 
0.1%
31075
 
0.1%
381050
 
0.1%
72011042
 
0.1%
Other values (6660)177922
 
20.5%
ValueCountFrequency (%)
-19146
 
1.1%
0669327
77.2%
12119
 
0.2%
2576
 
0.1%
31075
 
0.1%
41713
 
0.2%
51084
 
0.1%
61675
 
0.2%
7197
 
< 0.1%
8517
 
0.1%
ValueCountFrequency (%)
9999386
< 0.1%
99651
 
< 0.1%
99621
 
< 0.1%
99321
 
< 0.1%
99311
 
< 0.1%
98981
 
< 0.1%
98741
 
< 0.1%
98621
 
< 0.1%
98381
 
< 0.1%
98323
 
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
None within 50 metres
862178 
Control by other authorised person
 
3084
Control by school crossing patrol
 
2110

Length

Max length34
Median length21
Mean length21.07541401
Min length21

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone within 50 metres
2nd rowNone within 50 metres
3rd rowNone within 50 metres
4th rowNone within 50 metres
5th rowNone within 50 metres

Common Values

ValueCountFrequency (%)
None within 50 metres862178
99.4%
Control by other authorised person3084
 
0.4%
Control by school crossing patrol2110
 
0.2%

Length

2022-01-09T17:28:49.550565image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-09T17:28:49.653631image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
none862178
24.8%
within862178
24.8%
50862178
24.8%
metres862178
24.8%
control5194
 
0.1%
by5194
 
0.1%
other3084
 
0.1%
authorised3084
 
0.1%
person3084
 
0.1%
school2110
 
0.1%
Other values (2)4220
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
No physical crossing within 50 meters
726800 
Pedestrian phase at traffic signal junction
 
56767
non-junction pedestrian crossing
 
44220
Zebra crossing
 
22687
Central refuge
 
14528

Length

Max length43
Median length37
Mean length36.10449842
Min length14

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo physical crossing within 50 meters
2nd rowZebra crossing
3rd rowNo physical crossing within 50 meters
4th rowPedestrian phase at traffic signal junction
5th rowNo physical crossing within 50 meters

Common Values

ValueCountFrequency (%)
No physical crossing within 50 meters726800
83.8%
Pedestrian phase at traffic signal junction56767
 
6.5%
non-junction pedestrian crossing44220
 
5.1%
Zebra crossing22687
 
2.6%
Central refuge14528
 
1.7%
Footbridge or subway2370
 
0.3%

Length

2022-01-09T17:28:49.772593image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-09T17:28:49.936630image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
crossing793707
16.1%
no726800
14.8%
physical726800
14.8%
within726800
14.8%
50726800
14.8%
meters726800
14.8%
pedestrian100987
 
2.1%
junction56767
 
1.2%
signal56767
 
1.2%
traffic56767
 
1.2%
Other values (9)216607
 
4.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Light_Conditions
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
Daylight: Street light present
635352 
Darkness: Street lights present and lit
172034 
Darkeness: No street lighting
 
48722
Darkness: Street lighting unknown
 
7594
Darkness: Street lights present but unlit
 
3670

Length

Max length41
Median length30
Mean length31.80169062
Min length29

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDaylight: Street light present
2nd rowDaylight: Street light present
3rd rowDaylight: Street light present
4th rowDaylight: Street light present
5th rowDarkness: Street lights present and lit

Common Values

ValueCountFrequency (%)
Daylight: Street light present635352
73.3%
Darkness: Street lights present and lit172034
 
19.8%
Darkeness: No street lighting48722
 
5.6%
Darkness: Street lighting unknown7594
 
0.9%
Darkness: Street lights present but unlit3670
 
0.4%

Length

2022-01-09T17:28:50.093631image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-09T17:28:50.193634image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
street867372
22.7%
present811056
21.2%
daylight635352
16.6%
light635352
16.6%
darkness183298
 
4.8%
lights175704
 
4.6%
and172034
 
4.5%
lit172034
 
4.5%
lighting56316
 
1.5%
darkeness48722
 
1.3%
Other values (4)63656
 
1.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Weather_Conditions
Categorical

HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
Fine without high winds
695531 
Raining without high winds
98724 
Other
 
21309
Unknown
 
16222
Fine with high winds
 
10859
Other values (4)
 
24727

Length

Max length26
Median length23
Mean length22.52156745
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFine without high winds
2nd rowFine without high winds
3rd rowFine without high winds
4th rowOther
5th rowFine without high winds

Common Values

ValueCountFrequency (%)
Fine without high winds695531
80.2%
Raining without high winds98724
 
11.4%
Other21309
 
2.5%
Unknown16222
 
1.9%
Fine with high winds10859
 
1.3%
Raining with high winds10782
 
1.2%
Snowing without high winds7928
 
0.9%
Fog or mist4937
 
0.6%
Snowing with high winds1080
 
0.1%

Length

2022-01-09T17:28:50.354592image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-09T17:28:50.453591image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
high824904
24.6%
winds824904
24.6%
without802183
23.9%
fine706390
21.1%
raining109506
 
3.3%
with22721
 
0.7%
other21309
 
0.6%
unknown16222
 
0.5%
snowing9008
 
0.3%
fog4937
 
0.1%
Other values (2)9874
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Road_Surface_Conditions
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
Dry
598877 
Wet/Damp
239644 
Frost/Ice
 
20727
Snow
 
7148
Flood (Over 3cm of water)
 
976

Length

Max length25
Median length3
Mean length4.557811412
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDry
2nd rowWet/Damp
3rd rowDry
4th rowFrost/Ice
5th rowDry

Common Values

ValueCountFrequency (%)
Dry598877
69.0%
Wet/Damp239644
27.6%
Frost/Ice20727
 
2.4%
Snow7148
 
0.8%
Flood (Over 3cm of water)976
 
0.1%

Length

2022-01-09T17:28:50.737246image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-09T17:28:50.822211image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
dry598877
68.7%
wet/damp239644
27.5%
frost/ice20727
 
2.4%
snow7148
 
0.8%
flood976
 
0.1%
over976
 
0.1%
3cm976
 
0.1%
of976
 
0.1%
water976
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
None
845719 
Roadworks
 
10308
Ol or diesel
 
3107
Mud
 
2606
Road surface defective
 
2163
Other values (3)
 
3469

Length

Max length47
Median length4
Mean length4.250095691
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone
2nd rowNone
3rd rowNone
4th rowNone
5th rowNone

Common Values

ValueCountFrequency (%)
None845719
97.5%
Roadworks10308
 
1.2%
Ol or diesel3107
 
0.4%
Mud2606
 
0.3%
Road surface defective2163
 
0.2%
Auto traffic singal out1654
 
0.2%
Permanent sign or marking defective or obscured1336
 
0.2%
Auto traffic signal partly defective479
 
0.1%

Length

2022-01-09T17:28:50.942213image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-09T17:28:51.082653image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
none845719
94.7%
roadworks10308
 
1.2%
or5779
 
0.6%
defective3978
 
0.4%
ol3107
 
0.3%
diesel3107
 
0.3%
mud2606
 
0.3%
road2163
 
0.2%
surface2163
 
0.2%
traffic2133
 
0.2%
Other values (9)11743
 
1.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
None
851300 
Other object in carriageway
 
7092
Any animal (except a ridden horse)
 
4685
Pedestrian in carriageway (not injured)
 
1950
Involvement with previous accident
 
1395

Length

Max length39
Median length4
Mean length4.513177737
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone
2nd rowNone
3rd rowNone
4th rowNone
5th rowNone

Common Values

ValueCountFrequency (%)
None851300
98.1%
Other object in carriageway7092
 
0.8%
Any animal (except a ridden horse)4685
 
0.5%
Pedestrian in carriageway (not injured)1950
 
0.2%
Involvement with previous accident1395
 
0.2%
Dislodged vehicle load in carriageway950
 
0.1%

Length

2022-01-09T17:28:51.383648image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-09T17:28:51.537649image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
none851300
91.7%
in9992
 
1.1%
carriageway9992
 
1.1%
object7092
 
0.8%
other7092
 
0.8%
a4685
 
0.5%
ridden4685
 
0.5%
horse4685
 
0.5%
except4685
 
0.5%
animal4685
 
0.5%
Other values (11)18965
 
2.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Urban_or_Rural_Area
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
1
559565 
2
307776 
3
 
31

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1559565
64.5%
2307776
35.5%
331
 
< 0.1%

Length

2022-01-09T17:28:51.670648image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-09T17:28:51.770300image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1559565
64.5%
2307776
35.5%
331
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing2128
Missing (%)0.2%
Memory size1.7 MiB
True
699234 
False
166010 
(Missing)
 
2128
ValueCountFrequency (%)
True699234
80.6%
False166010
 
19.1%
(Missing)2128
 
0.2%
2022-01-09T17:28:51.874903image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

LSOA_of_Accident_Location
Categorical

HIGH CARDINALITY
MISSING

Distinct34140
Distinct (%)4.2%
Missing58466
Missing (%)6.7%
Memory size6.6 MiB
E01000004
 
1749
E01011365
 
921
E01004736
 
812
E01004764
 
702
E01008440
 
676
Other values (34135)
804046 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique580 ?
Unique (%)0.1%

Sample

1st rowE01002882
2nd rowE01002886
3rd rowE01002912
4th rowE01002871
5th rowE01002840

Common Values

ValueCountFrequency (%)
E010000041749
 
0.2%
E01011365921
 
0.1%
E01004736812
 
0.1%
E01004764702
 
0.1%
E01008440676
 
0.1%
E01005131592
 
0.1%
E01002444522
 
0.1%
E01001771455
 
0.1%
E01018648448
 
0.1%
E01023722441
 
0.1%
Other values (34130)801588
92.4%
(Missing)58466
 
6.7%

Length

2022-01-09T17:28:52.125775image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
e010000041749
 
0.2%
e01011365921
 
0.1%
e01004736812
 
0.1%
e01004764702
 
0.1%
e01008440676
 
0.1%
e01005131592
 
0.1%
e01002444522
 
0.1%
e01001771455
 
0.1%
e01018648448
 
0.1%
e01023722441
 
0.1%
Other values (34130)801588
99.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Year
Real number (ℝ≥0)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2007.933542
Minimum2005
Maximum2011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 MiB
2022-01-09T17:28:52.257773image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2005
5-th percentile2005
Q12006
median2009
Q32010
95-th percentile2011
Maximum2011
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.326534405
Coefficient of variation (CV)0.00115867102
Kurtosis-1.670700666
Mean2007.933542
Median Absolute Deviation (MAD)2
Skewness-0.04228736391
Sum1741625332
Variance5.412762339
MonotonicityNot monotonic
2022-01-09T17:28:52.358773image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2005198634
22.9%
2006189126
21.8%
2009163536
18.9%
2010154406
17.8%
2011151458
17.5%
200710212
 
1.2%
ValueCountFrequency (%)
2005198634
22.9%
2006189126
21.8%
200710212
 
1.2%
2009163536
18.9%
2010154406
17.8%
2011151458
17.5%
ValueCountFrequency (%)
2011151458
17.5%
2010154406
17.8%
2009163536
18.9%
200710212
 
1.2%
2006189126
21.8%
2005198634
22.9%

Interactions

2022-01-09T17:27:40.814678image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:25:01.093947image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:25:16.576097image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:25:44.557857image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:25:54.461438image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:26:04.684403image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:26:14.480245image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:26:25.147109image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:26:34.759818image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:26:43.321825image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:26:52.303921image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:27:01.997585image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:27:12.703678image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:27:21.667675image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:27:31.497676image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:27:41.449675image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:25:03.653524image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:25:17.279279image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:25:45.161942image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:25:55.185139image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:26:05.305406image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:26:15.077245image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:26:25.934654image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:26:35.418820image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:26:43.878819image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:26:52.867926image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:27:02.738583image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:27:13.311677image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:27:22.330680image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2022-01-09T17:25:48.476904image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2022-01-09T17:26:18.149518image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:26:29.465819image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:26:38.246821image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:26:46.638821image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2022-01-09T17:25:59.603172image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:26:09.936244image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2022-01-09T17:27:17.461713image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:27:27.270677image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:27:36.056680image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:27:46.170680image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:25:12.545488image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:25:40.450853image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:25:50.712434image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2022-01-09T17:26:57.496923image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2022-01-09T17:27:36.614677image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:27:46.800677image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:25:13.248753image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:25:41.165849image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:25:51.364436image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:26:01.181407image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:26:11.397246image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:26:20.771559image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:26:31.821822image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:26:40.463822image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:26:49.175822image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:26:58.084922image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:27:09.203924image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:27:18.677678image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:27:28.660680image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:27:37.371676image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:27:47.356677image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2022-01-09T17:26:41.020823image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:26:49.727821image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:26:58.659924image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:27:09.883927image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:27:19.308677image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:27:29.239676image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:27:37.970676image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:27:48.005680image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:25:14.570750image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:25:42.541850image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:25:52.542434image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2022-01-09T17:26:50.382705image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:26:59.265923image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:27:10.641924image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:27:19.877677image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:27:29.763676image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:27:38.597675image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:27:48.536680image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:25:15.227157image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2022-01-09T17:26:23.512522image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:26:33.681823image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:26:42.188823image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:26:50.944922image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:26:59.930923image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:27:11.482675image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:27:20.501682image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:27:30.353675image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:27:39.241677image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:27:49.094676image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:25:15.866372image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:25:43.893850image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:25:53.878440image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:26:03.992402image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:26:13.716289image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:26:24.209928image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:26:34.165822image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:26:42.768821image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:26:51.655929image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:27:01.309127image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:27:12.047680image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:27:21.118683image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:27:30.874676image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:27:39.884683image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2022-01-09T17:28:52.572777image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-01-09T17:28:52.994402image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-01-09T17:28:53.387954image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-01-09T17:28:53.745826image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-01-09T17:28:54.309820image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-01-09T17:27:54.662844image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-01-09T17:28:09.610970image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-01-09T17:28:33.549525image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-01-09T17:28:36.056993image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Accident_IndexLocation_Easting_OSGRLocation_Northing_OSGRLongitudeLatitudePolice_ForceAccident_SeverityNumber_of_VehiclesNumber_of_CasualtiesDateDay_of_WeekTimeLocal_Authority_(District)Local_Authority_(Highway)1st_Road_Class1st_Road_NumberRoad_TypeSpeed_limitJunction_DetailJunction_Control2nd_Road_Class2nd_Road_NumberPedestrian_Crossing-Human_ControlPedestrian_Crossing-Physical_FacilitiesLight_ConditionsWeather_ConditionsRoad_Surface_ConditionsSpecial_Conditions_at_SiteCarriageway_HazardsUrban_or_Rural_AreaDid_Police_Officer_Attend_Scene_of_AccidentLSOA_of_Accident_LocationYear
0200901BS70001524910.0180800.0-0.20134951.51227312211/1/2009515:1112E0900002060One way street30NaNGiveway or uncontrolled60None within 50 metresNo physical crossing within 50 metersDaylight: Street light presentFine without high windsDryNoneNone1YesE010028822009
1200901BS70002525050.0181040.0-0.19924851.514399122115/1/2009210:5912E0900002050Single carriageway30NaNGiveway or uncontrolled50None within 50 metresZebra crossingDaylight: Street light presentFine without high windsWet/DampNoneNone1YesE010028862009
2200901BS70003526490.0177990.0-0.17959951.48666813214/1/2009114:1912E090000203308Single carriageway30NaNGiveway or uncontrolled60None within 50 metresNo physical crossing within 50 metersDaylight: Street light presentFine without high windsDryNoneNone1YesE010029122009
3200901BS70004524800.0180300.0-0.20311051.50780412215/1/200928:1012E090000203402Single carriageway30NaNAutomatic traffic signal4450None within 50 metresPedestrian phase at traffic signal junctionDaylight: Street light presentOtherFrost/IceNoneNone1YesE010028712009
4200901BS70005526930.0177490.0-0.17344551.48207612216/1/2009317:2512E0900002033212Single carriageway30NaNAutomatic traffic signal33220None within 50 metresNo physical crossing within 50 metersDarkness: Street lights present and litFine without high windsDryNoneNone1YesE010028402009
5200901BS70006526060.0178730.0-0.18552551.49341513231/1/2009511:4812E0900002060Single carriageway30NaNGiveway or uncontrolled60None within 50 metresNo physical crossing within 50 metersDaylight: Street light presentFine without high windsDryNoneNone1YesE010028392009
6200901BS70007526580.0177270.0-0.17856151.48017712218/1/2009513:5812E0900002033220Single carriageway30NaNGiveway or uncontrolled33220None within 50 metresNo physical crossing within 50 metersDaylight: Street light presentFine without high windsDryNoneNone1YesE010028412009
7200901BS70008526550.0178580.0-0.17852451.49195713112/1/2009613:1812E0900002050Dual carriageway30NaNAutomatic traffic signal33218None within 50 metresPedestrian phase at traffic signal junctionDaylight: Street light presentFine without high windsDryNoneNone1YesE010028352009
8200901BS70009527310.0179100.0-0.16739551.49646013127/1/2009412:1512E0900002060Single carriageway30NaNGiveway or uncontrolled60None within 50 metresNo physical crossing within 50 metersDaylight: Street light presentFine without high windsDryNoneNone1YesE010028192009
9200901BS70010526250.0177370.0-0.18327551.481150131110/1/200979:5212E090000203308Single carriageway30NaNAutomatic traffic signal33220None within 50 metresNo physical crossing within 50 metersDaylight: Street light presentOtherWet/DampRoadworksNone1YesE010028432009

Last rows

Accident_IndexLocation_Easting_OSGRLocation_Northing_OSGRLongitudeLatitudePolice_ForceAccident_SeverityNumber_of_VehiclesNumber_of_CasualtiesDateDay_of_WeekTimeLocal_Authority_(District)Local_Authority_(Highway)1st_Road_Class1st_Road_NumberRoad_TypeSpeed_limitJunction_DetailJunction_Control2nd_Road_Class2nd_Road_NumberPedestrian_Crossing-Human_ControlPedestrian_Crossing-Physical_FacilitiesLight_ConditionsWeather_ConditionsRoad_Surface_ConditionsSpecial_Conditions_at_SiteCarriageway_HazardsUrban_or_Rural_AreaDid_Police_Officer_Attend_Scene_of_AccidentLSOA_of_Accident_LocationYear
867362200701MD68578533950.0171420.0-0.07470451.425910122131/12/200721:158E090000283212Dual carriageway30NaNGiveway or uncontrolled60None within 50 metresNo physical crossing within 50 metersDarkness: Street lights present and litFine without high windsWet/DampNoneNone1YesE010039482007
867363200701MD68647532660.0180170.0-0.08996751.504847132115/12/2007713:408E0900002833Single carriageway30NaNAutomatic traffic signal33200None within 50 metresNo physical crossing within 50 metersDaylight: Street light presentFine without high windsDryNoneNone1YesE010039292007
867364200701MD68794534840.0177530.0-0.05958351.480607122112/7/200758:298E0900002832Single carriageway30NaNGiveway or uncontrolled60None within 50 metresNo physical crossing within 50 metersDaylight: Street light presentOtherDryNoneNone1YesE010039882007
867365200701MD68800532460.0176980.0-0.09404351.47622613215/7/200750:018E090000283215Single carriageway30NaNAutomatic traffic signal60None within 50 metresPedestrian phase at traffic signal junctionDarkness: Street lights present and litRaining without high windsWet/DampNoneNone1NoE010039212007
867366200701MD68803535180.0177340.0-0.05476251.478818131120/07/2007612:308E0900002850Dual carriageway30NaNGiveway or uncontrolled32None within 50 metresNo physical crossing within 50 metersDaylight: Street light presentRaining without high windsWet/DampNoneNone1YesE010039842007
867367200701MD68807531780.0178620.0-0.10321751.491123122111/5/200767:538E0900002833Single carriageway30NaNAutomatic traffic signal33204None within 50 metresPedestrian phase at traffic signal junctionDaylight: Street light presentFine without high windsDryNoneNone1NoE010031102007
867368200701MD68808532110.0177000.0-0.09907251.476488132115/05/200739:008E090000283202Single carriageway30NaNGiveway or uncontrolled60None within 50 metresNo physical crossing within 50 metersDaylight: Street light presentFine without high windsDryNoneNone1YesE010039192007
867369200701MD68812532390.0173070.0-0.09651351.441104132117/05/2007520:559E0900002250Single carriageway30NaNGiveway or uncontrolled60None within 50 metresNo physical crossing within 50 metersDaylight: Street light presentUnknownDryNoneNone1NoE010031702007
867370200701MD68824531950.0180250.0-0.10016151.50573213218/5/2007317:508E0900002833200Single carriageway30NaNNo_Junction_Control-10None within 50 metresNo physical crossing within 50 metersDaylight: Street light presentFine with high windsDryNoneNone1YesE010039272007
867371200701MD68851533630.0179100.0-0.07640451.495003121114/11/2007410:458E0900002832206Single carriageway30NaNGiveway or uncontrolled50None within 50 metresZebra crossingDaylight: Street light presentFine without high windsDryNoneNone1YesE010039782007